DATA608: Module 3

library(tidyverse)
library(plotly)
library(shiny)
library(rsconnect)

Module 3

I have provided you with data about mortality from all 50 states and the District of Columbia. Please access it at https://github.com/charleyferrari/CUNY_DATA608/tree/master/module3/data You are invited to gather more data from our provider, the CDC WONDER system, at https://wonder.cdc.gov/ucd-icd10.html.

Load Data

Load data from the course github.

data <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module3/data/cleaned-cdc-mortality-1999-2010-2.csv", header = TRUE)

summary(data)

##  ICD.Chapter           State                Year          Deaths     
##  Length:9961        Length:9961        Min.   :1999   Min.   :   10  
##  Class :character   Class :character   1st Qu.:2002   1st Qu.:  177  
##  Mode  :character   Mode  :character   Median :2005   Median :  667  
##                                        Mean   :2005   Mean   : 2929  
##                                        3rd Qu.:2008   3rd Qu.: 2474  
##                                        Max.   :2010   Max.   :96511  
##    Population         Crude.Rate    
##  Min.   :  491780   Min.   :  0.00  
##  1st Qu.: 1728292   1st Qu.:  4.60  
##  Median : 4219239   Median : 24.00  
##  Mean   : 5937896   Mean   : 52.15  
##  3rd Qu.: 6562231   3rd Qu.: 50.50  
##  Max.   :37253956   Max.   :478.40

causes <- data %>% 
  select(ICD.Chapter) %>%
  distinct() %>%
  arrange(ICD.Chapter)

years <- data %>%
  select(Year) %>%
  distinct() %>%
  arrange(Year)

states <- data %>%
  select(State) %>%
  distinct() %>%
  arrange()

Question 1

As a researcher, you frequently compare mortality rates from particular causes across different States. You need a visualization that will let you see (for 2010 only) the crude mortality rate, across all States, from one cause (for example, Neoplasms, which are effectively cancers). Create a visualization that allows you to rank States by crude mortality for each cause of death.

filter_year = 2010
filter_cat = 'Diseases of the circulatory system'

data %>%
  filter(Year == filter_year & ICD.Chapter == filter_cat) %>%
  plot_ly() %>%
  add_trace(x = ~Crude.Rate, y = ~State, type = 'bar') %>%
  layout(
    title = paste(filter_year, filter_cat, sep = ' '),
    barmode = 'group',
    xaxis = list(title = 'Mortality Rate per 100k'),
    yaxis = list(title = 'State', categoryorder = 'total descending')
  )

ui1 <- fluidPage(
  titlePanel('Mortality Rates by Cause of Death by Year'),
  sidebarLayout(
    sidebarPanel(
      selectInput(
        inputId = 'cause',
        label = 'Cause of Death:',
        choices = causes,
        selected = filter_cat),
      sliderInput(
        inputId = 'year',
        label = 'Year:',
        min = min(years),
        max = max(years),
        step = 1,
        sep = '',
        ticks = FALSE,
        value = filter_year),
      width = 3),
    mainPanel(
      plotlyOutput('plot')
    )
  )
)

server1 <- function(input, output) {
  output$plot <- renderPlotly({
    data %>% 
      filter(Year == input$year & ICD.Chapter == input$cause) %>%
      plot_ly() %>%
      add_trace(x = ~Crude.Rate, y = ~State, type = 'bar') %>%
      layout(
        title = paste(input$cause, 'by State for',input$year, sep = ' '),
        barmode = 'group',
        xaxis = list(title = 'Mortality Rate per 100k'),
        yaxis = list(title = 'State', categoryorder = 'total descending')
      )
  })
}

shinyApp(ui1, server1)

Shiny applications not supported in static R Markdown documents

Question 2

Often you are asked whether particular States are improving their mortality rates (per cause) faster than, or slower than, the national average. Create a visualization that lets your clients see this for themselves for one cause of death at the time. Keep in mind that the national average should be weighted by the national population.

Calculate the national average for each year and cause of death.

data <- data %>%
  group_by(ICD.Chapter, Year) %>%
  mutate(USAvg = 100000 * sum(Deaths) / sum(Population))

ui2 <- fluidPage(
  titlePanel('Mortality Rates by Cause of Death by Year'),
  sidebarLayout(
    sidebarPanel(
      selectInput(
        inputId = 'cause',
        label = 'Cause of Death:',
        choices = causes,
        selected = filter_cat),
      selectInput(
        inputId = 'state',
        label = 'State:',
        choices = states),
      width = 3),
    mainPanel(
      plotlyOutput('plot')
    )
  )
)

server2 <- function(input, output) {
  output$plot <- renderPlotly({
    data %>% 
      filter(State == input$state & ICD.Chapter == input$cause) %>%
      plot_ly() %>%
      add_trace(x = ~Year, y = ~Crude.Rate, name = input$state, type = 'scatter', mode = 'lines+markers') %>%
      add_trace(x = ~Year, y = ~USAvg, name = 'US Average', type = 'scatter', mode = 'lines+markers') %>%
      layout(
        title = paste(input$cause, sep = ' '),
        xaxis = list(title = 'Year'),
        yaxis = list(title = 'Mortality Rate per 100k')
      )
  })
}

shinyApp(ui2, server2)

Shiny applications not supported in static R Markdown documents